Computational Investing, Part I
School: Georgia Tech
Registration Link: CLICK HERE
Start Date: June 6, 2016
Workload: 8-12 Hours Per Week
Instructor: Tucker Balch
Credentials: Balch is an associate professor in Georgia Tech’s School of Interactive Computing. The author of over 80 research articles and recipient of nearly 20 grants over his career, Balch also serves as the co-found and CTO of Lucena Research.
Graded: Students can choose to explore course videos, discussions, and ungraded assignments for free, but they won’t be able to sit for the final exam or earn a certificate without paying a course fee.
Description: Investors often make the same mistakes. Sometimes, they cannot process data fast enough to act at the speed of the market. Other times, they respond emotionally and chuck their long-term strategy in the heat of the moment. These days, investors can rely on tools related to high frequency or algorithmic trading to increase their speed and maintain consistency. In this course, Balch will introduce students to the “principles and algorithms that hedge funds and investment professionals use to maximize return and reduce risk in equity portfolios.” In addition, the course will answer these questions:
- Why do the prices of some companies’ stocks seem to move up and down together while others move separately?
- What does portfolio “diversification” really mean and how important is it?
- What should the price of a stock be? How can we discover and exploit the relationships between equity prices automatically?
The course is divided into eight modules:
- Portfolio Management and Market Mechanics From a Portfolio Manager’s perspective
- Company Worth, Capital Assets Pricing Model and QSTK Software Overview
- Manipulating Data in Python and QSTK To Create a Portfolio
- Efficient Markets Hypothesis and Event Studies, Portfolio Optimization and the Efficient Frontier.
- Reading an Event Study and Detecting Wrong Data
- The Fundamental Law, CAPM for Portfolios
- Information Feeds and Technical Analysis
- Jensen’s Alpha, Back Testing and Machine Learning
Review: “Great course if you have a technical background. If you don’t know python programming it’ll be a bit hard. But if you know python its a good introduction to computational investing. The one thing I’ll remove from the course is the videos answering questions from the people taking the course.” For additional reviews, click here.